# Developing Robust Digital Twins and Reinforcement Learning for Accelerator Control Systems at the Fermilab Booster

arXiv: https://arxiv.org/abs/2105.12847 · HTML: https://mlsanity.com/paper/2105.12847/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 May 2021.

## Built on by (most cited first, 6 of 6)

- [Machine Learning in Nuclear Physics](https://mlsanity.com/paper/2112.02309/) (2021)
- [Robust Errant Beam Prognostics with Conditional Modeling for Particle Accelerators](https://mlsanity.com/paper/2312.10040/) (2023)
- [Digital Twin AI: Opportunities and Challenges from Large Language Models to World Models](https://mlsanity.com/paper/2601.01321/) (2026)
- [Human-in-the-loop Reinforcement Learning for Data Quality Monitoring in Particle Physics Experiments](https://mlsanity.com/paper/2405.15508/) (2024)
- [Outlook Towards Deployable Continual Learning for Particle Accelerators](https://mlsanity.com/paper/2504.03793/) (2025)
- [Harnessing the Power of Gradient-Based Simulations for Multi-Objective Optimization in Particle Accelerators](https://mlsanity.com/paper/2411.04817/) (2024)

## Abstract (arXiv)

We describe the offline machine learning (ML) development for an effort to precisely regulate the Gradient Magnet Power Supply (GMPS) at the Fermilab Booster accelerator complex via a Field-Programmable Gate Array (FPGA). As part of this effort, we created a digital twin of the Booster-GMPS control system by training a Long Short-Term Memory (LSTM) to capture its full dynamics. We outline the path we took to carefully validate our digital twin before deploying it as a reinforcement learning (RL) environment. Additionally, we demonstrate the use of a Deep Q-Network (DQN) policy model with the capability to regulate the GMPS against realistic time-varying perturbations.